Measuring the appearance similarity in Person Re-Identification is a challenging task which not only requires
the selection of discriminative visual descriptors but also their optimal combination. This paper presents a
unified learning framework composed by Deep Convolutional Neural Networks to simultaneously and automatically
learn the most salient features for each one of nine different body parts and their best weighting to
form a person descriptor. Moreover, to cope with the cross-view variations, these have been coded in a Mahalanobis
Matrix, in an adaptive process, also integrated into the learning framework, which takes advantage
of the discriminative information given by the dataset labels to analyse the data structure. The effectiveness
of the proposed approach, named Deep Parts Similarity Learning (DPSL), has been evaluated and compared
with other state-of-the-art approaches over the challenging PRID2011 dataset.

Bermejo-Alonso and colleagues (Bermejo-Alonso et al., 2018) define an ontology for tasks and planning in the autonomous system domain. It focuses on an emergency scenario for Unmanned Ground Vehicles (UGVs) or Unmanned Aerial Vehicles (UAVs). In this context, it is necessary to define how the autonomous system will act, detailing how the actions should be done to achieve system goals. The planning process starts with detailing the planning domain knowledge: the initial state, the goals, the actors, the resources, etc. This domain knowledge is then fed into a planner that, if a solution exists, will produce a plan or a set of plans to be used by the robotic system. Ontologies are a useful way to provide this domain knowledge and can be used to characterise the planning domain knowledge. However, there is a number of available ontologies for planning, being unclear which one is best for autonomous systems. This paper presents a review of existing task and planning vocabularies, taxonomies and ontologies, as a necessary first step in an ontology engineering process that addressed the autonomous system planning needs. This paper describes the analised ontologies, their main features, and how the process to integrate them was carried out.

The selection of discriminative features that properly define a person appearance is one of the current challenges for person re-identification. This paper presents a three-dimensional representation to compare person images, which is based on the similarity, independently measured for the head, upper body, and legs from two images. Three deep Siamese neural networks have been implemented to automatically find salient features for each body part. One of the main problems in the learning of features for re-identification is the presence of intra-class variations and inter-class ambiguities. This paper proposes a novel normalized double-margin-based contrastive loss function for the training of Siamese networks, which not only improves the robustness of the learned features against the mentioned problems but also reduce the training time. A comparative evaluation over the challenging PRID 2011 dataset has been conducted, resulting in a remarkable enhancement of the single-shot re-identification performance thanks to the use of our descriptor based on deeply learned features in comparison with the employment of low-level features. The obtained results also show the improvements generated by our normalized double-margin-based function with respect to the traditional contrastive loss function.

Interest on autonomous vehicles has rapidly increased in the last few years, due to recent advances in the
field and the appearance of semi-autonomous solutions in the market. In order to reach fully autonomous
navigation, a precise understanding of the vehicle surroundings is required. This paper presents a novel ROS-based
architecture for stereo-vision-based semantic scene labelling. The objective is to provide the necessary
information to a path planner in order to perform autonomous navigation around the university campus. The
output of the algorithm contains the classification of the obstacles in the scene into four different categories:
traversable areas, garden, static obstacles, and pedestrians. Validation of the labelling method is accomplished
by means of a hand-labelled ground truth, generated from a stereo sequence captured in the university campus.
The experimental results show the high performance of the proposed approach.

The Systems Modeling Language (SysML) is spreading very fast. Most modelling tool vendors support it and
practitioners have adopted it for Systems Engineering. The number of SysML models is growing, increasing
the need for and the potential benefit from platforms that allow a user to reuse the knowledge represented in
the models. However, SysML model reuse remains challenging. Each tool has its own implementation of
SysML, hindering reuse between tools. The search capabilities of most tools are also very limited and finding
reusable models can be difficult. This paper presents our vision and initial work towards enabling an effective
reuse of the knowledge contained in SysML models. The proposed solution is based on a universal
information representation model called RSHP and on existing technology for indexing and retrieval. The
solution has been used to index models of all SysML diagram types and preliminary validated with
requirements diagrams. The results from the validation show that the solution has very high precision and
recall. This makes us confident that the solution can be a suitable means for effective SysML model reuse.

This work describes recent advances in the analysis of driver aggressiveness in real road environments, based on on-board sensors, and Inertial Measurement Unit (IMU) with GPS information. In order to provide driver behaviour identification, a low-cost hardware architecture had been developed to retrieve Controller Area Network (CAN) Bus information. These data, combined with the IMU and the GPS, allow to provide driver behaviour identification. Therefore, features such as steering angle, throttle pressed percentage, linear accelerations, etc. are fused to classify driver behaviour through an expert system. This development has been exposed in real-traffic situations, with 10 different drivers. Tool showed, will allow researchers, drivers, and insurance companies to better understand risky driving behaviours.

Autonomous navigation for unmanned ground vehicles has gained significant interest in the research community of mobile robotics. This increased attention comes from its noteworthy role in the field of Intelligent Transportation Systems (ITS). In order to achieve the autonomous navigation for ground vehicles, a detailed model of the environment is required as its input map. This paper presents a novel approach to recognize static obstacles by means of an on-board stereo camera and build a local occupancy grid map in a Robot Operating System (ROS) architecture. The output maps include information concerning the environment 3D structures, which is based on stereo vision. These maps can enhance the global grid map with further details for the undetected obstacles by the laser rangefinder. In order to evaluate the proposed approach, several experiments are performed in different scenarios. The output maps are precisely compared to the corresponding global map segment and to the equivalent satellite image. The obtained results indicate the high performance of the approach in numerous situations.

Nowadays, there are many scientific inventions referring to any topic like medicine, technology, economics, finance, banking, computer science, and so on. These inventions are suggested as patents to the agencies working in US and Europe for the registration and revision of the patent applications. But, the job of reviewing the patents might be complicated because every day the quantity of it is bigger and bigger. And also, the amount of work dedicated writing a proper application might be intricate and needs several revisions from investor and examiners. This revision job might have costs for the inventor because they don’t know the proper language for writing the application in the formal mode used. As part of a solution, one approach to minimize the impact of this fact and increase the success of the reviewing process is aid the human reviewer and also inventors with a set of patterns created using Natural Language Processing techniques that accelerate the review just looking in the massive set of registration any similar one already patented and on the other hand aid the inventor writing in the formal manner the application.

State of the art Driving Assistance Systems and Autonomous Driving applications are employing sensor fusion in order to achieve trustable obstacle detection and classification under any meteorological and illumination condition. Fusion between laser and camera is widely used in ADAS applications in order to overcome the difficulties and limitations inherent to each of the sensors. In the system presented, some novel techniques for automatic and unattended data alignment are used and laser point clouds are exploited using Artificial Intelligence techniques to improve the reliability of the obstacle classification. New approaches to the problem of clustering sparse point clouds have been adopted, maximizing the information obtained from low resolution lasers. After improving cluster detection, AI techniques have been used to classify the obstacle not only with vision, but also with laser information. The fusion of the information acquired from both sensors, adding the classification capabilities of the laser, improves the reliability of the system.

Nowadays, there are many scientific articles referring to any topic like medicine, technology, economics,
finance, and so on. These articles are better known as papers, they represent the evaluation and
interpretation of different arguments, showing results of scientific interest. At the end, most of these are
published in magazines, books, journals, etc. Due to the fact that these papers are created with a higher
frequency it is feasible to analyse how people write in the same domain. At the level of structure and with
the help of graphs some of the results that can be found are: groups of words that are used (to determine if
they come from a specific vocabulary), most common grammatical categories, most repeated words in a
domain, patterns found, and frequency of patterns found. This research has been created to fulfil these
needs. A domain of public health has been selected and it is composed of 800 papers about different topics
referring to genetics such as mutations, genetic deafness, DNA, trinucleotide, suppressor genes, among
others; and an ontology of public health has been used to provide the basis of the study.